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E-raamat: Fault Diagnosis for Robust Inverter Power Drives

Edited by (SmartWires, USA)
  • Formaat: PDF+DRM
  • Sari: Energy Engineering
  • Ilmumisaeg: 10-Dec-2018
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781785614118
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Fault Diagnosis for Robust Inverter Power DrivesSuurem pilt
  • Formaat: PDF+DRM
  • Sari: Energy Engineering
  • Ilmumisaeg: 10-Dec-2018
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781785614118
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Fault Diagnosis for Robust Inverter Power Drives focuses on early diagnosis, prognosis, and intrinsic reliability of inverter power drives and their applications. Topics include material degradation, materials, semiconductors, inverter topologies, and early diagnosis as well as fault tolerant software strategies.



Power drives are used for induction motor control, uninterruptible power supplies, and in electrical vehicles. The increasing penetration of power drives makes their reliability, robustness, and early diagnosis a central point of attention especially in planning, designing, and financing. This book explores fault diagnosis of inverter drives in order to prevent malfunction and inefficient operation.

Fault Diagnosis for Robust Inverter Power Drives focuses on early diagnosis, prognosis, and intrinsic reliability of inverter power drives and their applications. Topics include material degradation, materials, semiconductors, inverter topologies, and early diagnosis as well as fault tolerant software strategies.

This work is highly relevant to researchers, power electronics professionals, and system designers in aerospace, hybrid and electrical cars, and power systems.

Preface xiii
1 Fundamentals for reliability and early diagnosis for inverter power drives
1(34)
Jose Manuel Aller
Antonio Ginart
George Vachtsevanos
1.1 Introduction
1(2)
1.1.1 Manufacture defects (early failure)
2(1)
1.1.2 Random failure
2(1)
1.1.3 Wear-out failure
3(1)
1.2 Statistical life estimation and failure rate: the bathtub curve
3(3)
1.2.1 Reliability R(t) and unreliability F(t) functions
3(1)
1.2.2 Probability density function and medium time before failure
3(1)
1.2.3 Failure rate function
4(1)
1.2.4 Exponential distribution
4(1)
1.2.5 Weibull distribution
5(1)
1.3 Degradation, failure mechanisms, and life model estimation
6(4)
1.3.1 Solid-stare materials
6(2)
1.3.2 Failure modes and physics-based life model calculation
8(2)
1.4 Inverters failure and power drives
10(1)
1.5 Circuit with ideal switches: power switches fundamentals
11(2)
1.6 PWM, the enabler of power electronics
13(1)
1.7 Switching under RL circuit load
14(1)
1.8 RLC circuit
15(2)
1.8.1 Series RLC model
16(1)
1.8.2 Shunt RLC model
16(1)
1.9 PWM in inverters
17(1)
1.10 Inverter basic operation
17(2)
1.11 Three-phase and multilevel inverters
19(1)
1.12 Operation principle of multilevel inverters
20(1)
1.13 Dominant topology
20(2)
1.14 Resonant converters
22(1)
1.15 Real switches: power losses in hard switching
23(4)
1.15.1 Conduction losses
23(2)
1.15.2 Switching losses
25(2)
1.16 Thermal consideration
27(8)
1.16.1 State modeling of the thermal system
27(2)
1.16.2 Thermal runaway
29(2)
References
31(4)
2 Early diagnosis in power semiconductors: MOSFET, IGBT, emerging materials (SiC and GaNs)
35(34)
Antonio Ginart
Jose M. Aller
George J. Vachtsevanos
2.1 Introduction
35(7)
2.1.1 Power device stress factors
36(1)
2.1.2 Silicon power MOSFET structure and parasitics
37(1)
2.1.3 SiC power MOSFET structure and parasitics
38(1)
2.1.4 GaNs structure and parasitics
39(1)
2.1.5 IGBT structure and latch-up
40(2)
2.2 Switching process in semiconductors
42(3)
2.2.1 Field distortion acceleration model
44(1)
2.3 Relevant indicators in power semiconductors
45(24)
2.3.1 Voltage Vth and capacitance shift
46(3)
2.3.2 Ringing characterization and turn-on delay
49(5)
2.3.3 Detachment and wire bond fatigue
54(4)
2.3.4 Junction temperature of power semiconductor
58(6)
References
64(5)
3 Early diagnosis in DC-link capacitors: electrolytic and films
69(34)
Chelan Kulkarni
Jose Celaya
Kai Goebel
3.1 Introduction
69(3)
3.1.1 Research challenges
71(1)
3.1.2 Organization
71(1)
3.2 Modeling for prognostics
72(1)
3.3 Research methodology
73(1)
3.4 Degradation in electrolytic capacitors
74(7)
3.4.1 Degradation mechanisms
75(2)
3.4.2 Capacitor degradation models
77(1)
3.4.3 Physics-based models for C and ESR
78(2)
3.4.4 Time-dependent degradation models
80(1)
3.5 Model-based prognostics framework
81(7)
3.5.1 Kalman filter for state estimation
82(1)
3.5.2 Future state forecasting
82(1)
3.5.3 Noise models
82(1)
3.5.4 Prognostics problem formulation
83(1)
3.5.5 Physics-based modeling framework using unscented Kalman filter
83(5)
3.6 Accelerated aging experiments
88(4)
3.6.1 Experimental setup
89(1)
3.6.2 Electrical overstress
90(1)
3.6.3 EOS experiment
90(2)
3.7 Prediction of remaining useful life results and validation tests
92(6)
3.7.1 Results for capacitor degradation model (D4)
93(3)
3.7.2 Results for ESR degradation model (Ds)
96(2)
3.8 Conclusion
98(5)
References
99(4)
4 Embedded fault diagnosis and prognosis
103(38)
Julio Viola
Jose Restrepo
Ronald Harley
4.1 Introduction
103(1)
4.2 Embedded systems
103(1)
4.3 Diagnosis, prognosis, and condition monitoring
104(1)
4.4 Review of hardware used in embedded diagnosis and prognosis systems
104(4)
4.4.1 Sensors
104(1)
4.4.2 Microprocessors, microcontrollers, and digital signal processors
105(2)
4.4.3 Analog-to-digital converters
107(1)
4.5 Switching devices and their faults
108(3)
4.6 Analysis of aging in IGBT power modules
111(1)
4.7 Prognosis and condition monitoring of power switches
112(4)
4.7.1 VCE monitoring
113(2)
4.7.2 ROS ON monitoring
115(1)
4.7.3 Other indicators
115(1)
4.8 Fault diagnosis techniques of power switches
116(7)
4.8.1 Open-circuit fault detection
117(4)
4.8.2 Short-circuit fault detection
121(2)
4.9 Fault prognosis and diagnosis in sources and loads
123(18)
4.9.1 PV arrays
124(2)
4.9.2 Detection of islanding in grid connected inverters
126(2)
4.9.3 Condition monitoring in electric machines
128(1)
4.9.4 State of health in batteries
129(2)
4.9.5 Fault diagnosis in sensors
131(1)
References
131(10)
5 Fault-tolerance strategies for power converters
141(3)
Jose A. Restrepo
Julio C. Viola
Ronald Harley
5.1 Fault prognosis/diagnosis and health management
144(1)
5.1.1 Condition monitoring of IGBTs
145(1)
5.1.2 Health prognosis of IGBTs
145(1)
5.1.3 Diagnosis
146(1)
5.2 Fault-tolerant topologies
146(1)
5.2.1 2L-VSI with middle-point connection
146(2)
5.2.2 Space vector generation during fault-tolerant operation
148(4)
5.2.3 Fault-tolerant operation of the back-to-back converter
152(4)
5.2.4 Three-level NPC converter
156(3)
5.3 Fault-tolerant operation of the open-end converter
159(17)
5.3.1 DTC algorithm
162(1)
5.3.2 DTC operation for switch trigger suppression
163(13)
5.4 Summary
176(9)
References
176(9)
6 Motor diagnostics and protection using inverter capabilities
185(68)
Stefan Grubic
Jose M. Aller
Thomas G. Habetler
6.1 Introduction
185(4)
6.2 Thermal monitoring and protection
189(10)
6.2.1 Thermal models
190(4)
6.2.2 Parameter-based temperature estimation
194(5)
6.3 Monitoring and protection of stator-related issues
199(19)
6.3.1 Turn insulation
200(9)
6.3.2 Primary insulation system
209(4)
6.3.3 Open stator winding faults and open-switch faults
213(3)
6.3.4 Stator core monitoring
216(2)
6.4 Rotor-related issues
218(16)
6.4.1 Rotor eccentricity
219(6)
6.4.2 Broken rotor bars and end rings
225(5)
6.4.3 Demagnetization
230(4)
6.5 Bearings, gearbox, and other mechanical problems
234(19)
6.5.1 Bearings faults
235(4)
6.5.2 Gearbox faults
239(3)
References
242(11)
7 Battery storage
253(18)
Andres Salazar Llinas
Antonio Ginart
Javad Mohammadpour Velni
7.1 Introduction
253(6)
7.1.1 Batteries principle of operation
253(2)
7.1.2 Li-ion batteries
255(2)
7.1.3 High power applications for Li-ion batteries
257(2)
7.2 Electrical model of Li-ion batteries
259(1)
7.3 Aging of Li-ion batteries
260(11)
7.3.1 Method for detection of aging in batteries using impedance measurement
261(7)
References
268(3)
8 Prognostics: a battery case study
271(24)
Bin Zhang
8.1 Introduction
271(2)
8.2 Lebesgue sampling-based fault diagnosis and prognosis
273(5)
8.2.1 Fault mechanism modeling
274(1)
8.2.2 Lebesgue sampling
275(1)
8.2.3 Lebesgue sampling-based diagnosis
276(1)
8.2.4 Lebesgue sampling-based prognosis
277(1)
8.3 Applications to batteries
278(3)
8.4 Performance metrics for prognosis
281(8)
8.4.1 Prognostic horizon
281(1)
8.4.2 Acceptable predictions
282(1)
8.4.3 α-λ Metrics
282(1)
8.4.4 Relative accuracy
283(1)
8.4.5 Convergence
283(1)
8.4.6 Performance score
284(1)
8.4.7 Experimental results
284(5)
8.5 Conclusion
289(6)
References
290(5)
Index 295
Antonio E. Ginart is principal R&D engineer at SmartWires. He serves as Affiliate Faculty Member of the College of Engineering of the University of Georgia and Adjunct Professor at Kennesaw State University. He has over 30 years of experience in power electronics, inverter drives design and motors control which has led to over 70 publications and patents.
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